This report is based on a model described in a paper presented at the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
If you want to cite the method/model please use:
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at the International Conference on Evolving Cities, MAST Mayflower Studios, Southampton, United Kingdom. 22 - 24 Sep 2021.
If you wish to re-use material from this report please cite it as:
Ben Anderson (2021) Simulating a local emissions levy to fund local energy effiency retrofit : Eastleigh. University of Southampton, United Kingdom
License: CC-BY
Share, adapt, give attribution.
This report estimates the value of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. These emissions are all consumption, gas and electricity. It does this under two scenarios - a simple carbon value multiplier and a rising block tariff.
It then compares these with estimates of the cost of retrofitting EPC band dwellings D-E and F-G in each LSOA and for the whole area under study.
Key results:
Background blurb about emissions, retofit, carbon tax/levy etc
In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator.
We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.
We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.
It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy value.
NB: no maps in the interests of speed
We will use a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).
All analysis is at LSOA level. Cautions on inference from area level data apply.
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."
Source: https://www.carbon.place/
Notes:
region | nLSOAs | mean_KgCo2ePerCap | sd_KgCo2ePerCap |
South East | 77 | 9,950.9 | 2,866.6 |
Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Eastleigh 006B Eastleigh South 1363 1182 1090
## 2: Eastleigh 010C Hedge End North 1318 1315 975
## 3: Eastleigh 010A Botley 1223 1044 808
## 4: Eastleigh 006A Eastleigh Central 1162 1378 1080
## 5: Eastleigh 013D Bursledon & Hound North 986 1071 798
## 6: Eastleigh 015A Hamble & Netley 978 1113 682
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Eastleigh 006A Eastleigh Central 1162 1378 1080
## 2: Eastleigh 010C Hedge End North 1318 1315 975
## 3: Eastleigh 006B Eastleigh South 1363 1182 1090
## 4: Eastleigh 015A Hamble & Netley 978 1113 682
## 5: Eastleigh 013D Bursledon & Hound North 986 1071 798
## 6: Eastleigh 014A Hamble & Netley 820 1052 639
LSOA11NM | WD18NM | nGasMeters | nElecMeters | epc_total |
Eastleigh 006B | Eastleigh South | 1,363 | 1,182 | 1,090 |
Eastleigh 010C | Hedge End North | 1,318 | 1,315 | 975 |
Eastleigh 010A | Botley | 1,223 | 1,044 | 808 |
Eastleigh 006A | Eastleigh Central | 1,162 | 1,378 | 1,080 |
Eastleigh 013D | Bursledon & Hound North | 986 | 1,071 | 798 |
Eastleigh 015A | Hamble & Netley | 978 | 1,113 | 682 |
Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.
Check that the assumption seems sensible…
Check for outliers - what might this indicate?
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values
| Name | …[] |
| Number of rows | 77 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e_pdw | 0 | 1 | 23274.81 | 7454.19 | 9361.12 | 18072.48 | 22307.69 | 28160.00 | 43104.48 | ▃▇▅▃▁ |
| CREDSgas_kgco2e2018_pdw | 0 | 1 | 2342.07 | 535.32 | 1392.33 | 1949.33 | 2228.83 | 2654.39 | 4308.85 | ▆▇▆▁▁ |
| CREDSelec_kgco2e2018_pdw | 0 | 1 | 1008.02 | 104.94 | 765.14 | 938.77 | 1014.59 | 1066.42 | 1394.64 | ▂▇▇▁▁ |
| CREDSmeasuredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3350.09 | 619.67 | 2326.83 | 2867.31 | 3136.61 | 3716.13 | 5703.50 | ▇▇▅▁▁ |
| CREDSotherEnergy_kgco2e2011_pdw | 0 | 1 | 54.18 | 47.20 | 0.00 | 22.14 | 40.61 | 64.87 | 293.32 | ▇▃▁▁▁ |
| CREDSallHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3404.27 | 616.19 | 2353.25 | 2922.74 | 3238.12 | 3748.90 | 5726.44 | ▆▇▃▁▁ |
| CREDScar_kgco2e2018_pdw | 0 | 1 | 2774.50 | 592.35 | 1645.25 | 2278.36 | 2766.77 | 3213.79 | 4436.34 | ▅▇▇▃▁ |
| CREDSvan_kgco2e2018_pdw | 0 | 1 | 439.43 | 462.26 | 104.00 | 234.04 | 304.78 | 419.09 | 3603.10 | ▇▁▁▁▁ |
| CREDSpersonalTransport_kgco2e2018_pdw | 0 | 1 | 3213.93 | 712.29 | 1947.87 | 2821.10 | 3233.36 | 3611.79 | 5958.19 | ▃▇▃▁▁ |
Examine patterns of per dwelling emissions for sense.
Figure 5.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.1: Scatter of LSOA level all per dwelling emissions against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -7.3429, df = 75, p-value = 2.102e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7605298 -0.4943403
## sample estimates:
## cor
## -0.6467139
## Total emissions per dwelling (LSOA level) summary
## LSOA11CD WD18NM IMD_Decile_label All_Tco2e_per_dw
## Length:77 Length:77 10 (10% least deprived):26 Min. : 9.361
## Class :character Class :character 9 :17 1st Qu.:18.072
## Mode :character Mode :character 8 :10 Median :22.308
## 6 : 8 Mean :23.275
## 7 : 6 3rd Qu.:28.160
## 3 : 3 Max. :43.104
## (Other) : 7
LSOA11CD | WD18NM | IMD_Decile_label | All_Tco2e_per_dw |
E01022693 | Hedge End North | 10 (10% least deprived) | 43.1 |
E01022702 | Hedge End South | 9 | 42.1 |
E01022706 | Hiltingbury | 10 (10% least deprived) | 39.0 |
E01022691 | Hedge End North | 10 (10% least deprived) | 36.7 |
E01022687 | Fair Oak & Horton Heath | 10 (10% least deprived) | 35.9 |
E01022694 | Hedge End North | 10 (10% least deprived) | 35.3 |
LSOA11CD | WD18NM | IMD_Decile_label | All_Tco2e_per_dw |
E01022649 | Bishopstoke | 3 | 9.4 |
E01022680 | Eastleigh South | 3 | 11.0 |
E01022711 | Hamble & Netley | 4 | 11.1 |
E01022690 | Hamble & Netley | 7 | 11.7 |
E01022677 | Eastleigh South | 6 | 12.2 |
E01022668 | Eastleigh Central | 6 | 13.4 |
Figure 5.2 uses the same plotting method to show emissions per dwelling due to gas use.
## Per dwelling T CO2e - gas emissions
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1392 1949 2229 2342 2654 4309
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.2: Scatter of LSOA level gas per dwelling emissions against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -6.6964, df = 75, p-value = 3.424e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7349777 -0.4492702
## sample estimates:
## cor
## -0.6116959
Figure 5.3 uses the same plotting method to show emissions per dwelling due to electricity use.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -4.4682, df = 75, p-value = 2.746e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6189272 -0.2613734
## sample estimates:
## cor
## -0.4585101
Figure 5.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -4.4682, df = 75, p-value = 2.746e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6189272 -0.2613734
## sample estimates:
## cor
## -0.4585101
RUC11 | mean_gas_kgco2e | mean_elec_kgco2e | mean_other_energy_kgco2e |
Rural town and fringe | 2,155.7 | 987.4 | 58.1 |
Urban city and town | 2,357.8 | 1,009.8 | 53.8 |
Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 7.1023, df = 75, p-value = 5.968e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4780478 0.7513870
## sample estimates:
## cor
## 0.6341288
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 6.8469, df = 75, p-value = 1.796e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4601305 0.7412099
## sample estimates:
## cor
## 0.6201926
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
How does the correlation look now?
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 5.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.5: Scatter of LSOA level car use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -8.8872, df = 75, p-value = 2.44e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.8102034 -0.5862536
## sample estimates:
## cor
## -0.7161906
RUC11 | mean_car_kgco2e | mean_van_kgco2e |
Rural town and fringe | 2,719.4 | 388.9 |
Urban city and town | 2,779.2 | 443.7 |
Figure 5.6 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.6: Scatter of LSOA level van use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = -0.55688, df = 75, p-value = 0.5793
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2840664 0.1621388
## sample estimates:
## cor
## -0.06417044
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 204.0 320.0 389.0 441.6 503.0 1090.0
## N elec meters
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 434 630 694 737 783 1378
Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Case studies:
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
Table 5.6 below shows the overall £ GBP total for the case study area in £M under Scenario 1.
nLSOAs | beis_GBPtotal_c | beis_total_c_gas | beis_GBPtotal_c_elec |
77 | 310.1 | 32.1 | 13.9 |
region | nLSOAs | beis_GBPtotal_c | beis_total_c_gas | beis_GBPtotal_c_elec |
South East | 77 | 310.1 | 32.1 | 13.9 |
Figure 5.7: Proportion of total emissions due to gas & electricity use by region covered
The table below shows the mean per dwelling value rounded to the nearest £10.
All_emissions | Gas | Electricity | Gas + Electricity |
5,702.3 | 573.8 | 247.0 | 820.8 |
Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.8: £k per LSOA revenue using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.9: £k per LSOA revenue using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2293 4428 5465 5702 6899 10561
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy |
E01022693 | Eastleigh 010D | Hedge End North | 43.1 | 10,560.6 |
E01022702 | Eastleigh 012F | Hedge End South | 42.1 | 10,311.7 |
E01022706 | Eastleigh 001B | Hiltingbury | 39.0 | 9,557.2 |
E01022691 | Eastleigh 010B | Hedge End North | 36.7 | 8,984.5 |
E01022687 | Eastleigh 008G | Fair Oak & Horton Heath | 35.9 | 8,798.5 |
E01022694 | Eastleigh 010E | Hedge End North | 35.3 | 8,649.3 |
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy |
E01022649 | Eastleigh 005E | Bishopstoke | 9.4 | 2,293.5 |
E01022680 | Eastleigh 006D | Eastleigh South | 11.0 | 2,689.0 |
E01022711 | Eastleigh 014B | Hamble & Netley | 11.1 | 2,711.0 |
E01022690 | Eastleigh 015C | Hamble & Netley | 11.7 | 2,865.3 |
E01022677 | Eastleigh 006B | Eastleigh South | 12.2 | 2,993.9 |
E01022668 | Eastleigh 006A | Eastleigh Central | 13.4 | 3,284.4 |
Figure ?? repeats the analysis but just for gas.
Anything unusual?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.10: £k per LSOA incurred via gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.11: £k per LSOA incurred via gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 341.1 477.6 546.1 573.8 650.3 1055.7
LSOA11CD | LSOA01NM | WD18NM | gas_T_CO2e_pdw | GBP_gas_levy_perdw |
E01022706 | Eastleigh 001B | Hiltingbury | 4.3 | 1,055.7 |
E01022705 | Eastleigh 001A | Hiltingbury | 3.5 | 847.3 |
E01022709 | Eastleigh 001E | Hiltingbury | 3.5 | 845.9 |
E01022663 | Eastleigh 003C | Chandler's Ford | 3.3 | 810.5 |
E01022707 | Eastleigh 001C | Hiltingbury | 3.2 | 781.1 |
E01022653 | Eastleigh 010A | Botley | 3.1 | 768.1 |
LSOA11CD | LSOA01NM | WD18NM | gas_T_CO2e_pdw | GBP_gas_levy_perdw |
E01022685 | Eastleigh 008E | Fair Oak & Horton Heath | 1.7 | 421.3 |
E01022669 | Eastleigh 007B | Eastleigh Central | 1.7 | 419.2 |
E01022668 | Eastleigh 006A | Eastleigh Central | 1.6 | 402.9 |
E01022680 | Eastleigh 006D | Eastleigh South | 1.5 | 378.5 |
E01022671 | Eastleigh 007D | Eastleigh North | 1.4 | 350.7 |
E01022716 | Eastleigh 009D | West End South | 1.4 | 341.1 |
Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.12: £k per LSOA incurred via electricity using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.13: £k per LSOA incurred via electricity using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 187.5 230.0 248.6 247.0 261.3 341.7
LSOA11CD | LSOA01NM | WD18NM | elec_T_CO2e_pdw | GBP_elec_levy_perdw |
E01022706 | Eastleigh 001B | Hiltingbury | 1.4 | 341.7 |
E01022658 | Eastleigh 013E | Bursledon & Hound North | 1.3 | 313.2 |
E01022682 | Eastleigh 008B | Fair Oak & Horton Heath | 1.2 | 297.6 |
E01022709 | Eastleigh 001E | Hiltingbury | 1.2 | 292.2 |
E01022705 | Eastleigh 001A | Hiltingbury | 1.2 | 292.0 |
E01022663 | Eastleigh 003C | Chandler's Ford | 1.2 | 283.4 |
LSOA11CD | LSOA01NM | WD18NM | elec_T_CO2e_pdw | GBP_elec_levy_perdw |
E01022679 | Eastleigh 007E | Eastleigh South | 0.9 | 213.4 |
E01022695 | Eastleigh 011B | Hedge End South | 0.9 | 212.9 |
E01022692 | Eastleigh 010C | Hedge End North | 0.9 | 209.2 |
E01022670 | Eastleigh 007C | Eastleigh Central | 0.9 | 209.1 |
E01022648 | Eastleigh 004A | Bishopstoke | 0.8 | 206.6 |
E01022677 | Eastleigh 006B | Eastleigh South | 0.8 | 187.5 |
Figure ?? shows the same analysis for measured energy (elec + gas)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.15: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 570.1 702.5 768.5 820.8 910.5 1397.4
Applied to per dwelling values (not LSOA total) - may be methodologically dubious?
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw
## 0% 25% 50% 75% 100%
## 9361.12 18072.48 22307.69 28160.00 43104.48
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
| Name | …[] |
| Number of rows | 77 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 23.27 | 7.45 | 9.36 | 18.07 | 22.31 | 28.16 | 43.10 | ▃▇▅▃▁ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 3982.23 | 2310.86 | 1142.06 | 2204.84 | 3195.73 | 5390.27 | 10874.89 | ▇▅▂▂▁ |
| beis_GBPtotal_sc2 | 0 | 1 | 2728200.12 | 1281957.64 | 734342.40 | 1774006.71 | 2392609.35 | 3368916.31 | 6138599.70 | ▇▇▅▂▂ |
nLSOAs | sum_total_sc1 | sum_total_sc2 |
77 | 310.1 | 210.1 |
Figure 5.16 compares the £ levy under each scenario for all consumption.
Figure 5.16: Comparing £ levy under each scenario
## [1] 20.62458
## [1] 8.456602
nLSOAs | sumAllConsEmissions_GBP | sumGasEmissions_GBP | sumElecEmissions_GBP |
77 | 210.1 | 20.6 | 8.5 |
region | nLSOAs | sumAllConsEmissions_GBP | sumGasEmissions_GBP | sumElecEmissions_GBP | sumPop |
South East | 77 | 210.1 | 20.6 | 8.5 | 131,840 |
## region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: South East 77 210.0714 20.62458 8.456602 131840
Figure 5.17: Contribution to sum levy £ GBP by source
Source: English Housing Survey 2018 Energy Report
Model excludes EPC A, B & C (assumes no need to upgrade)
Adding these back in would increase the cost… obvs
Table 5.13 reports total retofit costs.
## To retrofit D-E (£m)
## [1] 382.9268
## Number of dwellings: 28791
## To retrofit F-G (£m)
## [1] 25.70833
## Number of dwellings: 959
## To retrofit D-G (£m)
## [1] 408.6351
## To retrofit D-G (mean per dwelling)
## [1] 13733.26
meanPerLSOA_GBPm | total_GBPm |
5.3 | 408.6 |
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.18 shows the LSOA level retofit costs per dwelling by IMD decile.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.18: LSOA level retofit costs per dwelling by IMD score
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.19 shows years to pay under Scenario 1 (all emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.259 1.965 2.509 2.679 3.111 6.096
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.19: Years to pay under Scenario 1 (all em issions)
## Median years: 2.51
Figure 5.20 shows years to pay under Scenario 1 (energy emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.921 15.202 17.601 17.251 19.436 24.269
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.20: Years to pay under Scenario 1 (energy emissions)
## Median years: 17.6
Figure 5.21 shows the year 1 outcome if levy is shared equally (all emissions levy).
Figure 5.21: Year 1 outcome if levy is shared equally (all emissions levy)
LSOA11CD | LSOA11NM | WD18NM | retrofitSum | yearsToPay | epc_D_pc | epc_E_pc | epc_F_pc | epc_G_pc |
E01022704 | Eastleigh 002E | Hiltingbury | 8,882,341.8 | 15.2 | 0.6 | 0.2 | 0.0 | 0.0 |
E01022688 | Eastleigh 015A | Hamble & Netley | 8,185,772.3 | 15.5 | 0.4 | 0.1 | 0.0 | 0.0 |
E01022714 | Eastleigh 009B | West End North | 8,088,909.4 | 14.5 | 0.6 | 0.2 | 0.0 | 0.0 |
E01022708 | Eastleigh 001D | Hiltingbury | 7,738,365.0 | 15.3 | 0.6 | 0.2 | 0.0 | 0.0 |
E01022705 | Eastleigh 001A | Hiltingbury | 7,560,400.6 | 12.3 | 0.5 | 0.2 | 0.0 | 0.0 |
E01022661 | Eastleigh 003A | Chandler's Ford | 7,525,316.9 | 16.3 | 0.5 | 0.2 | 0.0 | 0.0 |
E01022710 | Eastleigh 014A | Hamble & Netley | 7,381,615.6 | 18.9 | 0.4 | 0.1 | 0.0 | 0.0 |
E01022690 | Eastleigh 015C | Hamble & Netley | 7,299,166.9 | 19.2 | 0.5 | 0.1 | 0.0 | 0.0 |
E01022706 | Eastleigh 001B | Hiltingbury | 6,930,270.3 | 9.9 | 0.6 | 0.2 | 0.0 | 0.0 |
E01022659 | Eastleigh 002A | Chandler's Ford | 6,705,929.3 | 17.9 | 0.6 | 0.2 | 0.0 | 0.0 |
Figure 5.22 shows the year 1 outcome if levy is shared equally (energy emissions levy).
Figure 5.22: Year 1 outcome if levy is shared equally (energy emissions levy)
What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…
Figure 5.23 shows years to pay under Scenario 2 (all emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.223 2.516 4.185 4.633 6.248 12.242
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.23: Years to pay under Scenario 2 (all em issions)
## Median years: 4.19
Figure 5.24 shows years to pay under Scenario 2 (energy emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.921 15.202 17.601 17.251 19.436 24.269
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.24: Years to pay under Scenario 2 (energy emissions)
Figure 5.25 shows the year 1 outcome if levy is shared equally (all emissions levy).
Figure 5.25: Year 1 outcome if levy is shared equally (all emissions levy)
Figure 5.26 shows the year 1 outcome if levy is shared equally (energy emissions levy).
Figure 5.26: Year 1 outcome if levy is shared equally (energy emissions levy)
What happens in Year 2 totally depends on the rate of upgrades…
Figure 5.27 compares pay-back times for the two scenarios - who does the rising block tariff help?
Figure 5.27: Comparing pay-back times across scenarios
x = y line shown for clarity
I don’t know if this will work…
## Doesn't